A Novel Self Adaptive Modification Approach Based on Bat Algorithm for Optimal Management of Renewable MG


In the new competitive electricity market, the accurate operation management of Micro-Grid (MG) with various types of renewable power sources (RES) can be an effective approach to supply the electrical consumers more reliably and economically. In this regard, this paper proposes a novel solution methodology based on bat algorithm to solve the op- timal energy management of MG including several RESs with the back-up of Fuel Cell (FC), Wind Turbine (WT), Photovoltaics (PV), Micro Turbine (MT) as well as storage devices to meet the energy mismatch. The problem is formulated as a nonlinear constraint optimization problem to minimize the total cost of the grid and RESs, simultaneously. In addition, the problem considers the interactive effects of MG and utility in a 24 hour time interval which would in- crease the complexity of the problem from the optimization point of view more severely. The proposed optimization technique is consisted of a self adaptive modification method compromised of two modification methods based on bat algorithm to explore the total search space globally. The superiority of the proposed method over the other well-known algorithms is demonstrated through a typical renewable MG as the test system.

Share and Cite:

A. Baziar, A. Kavoosi-Fard and J. Zare, "A Novel Self Adaptive Modification Approach Based on Bat Algorithm for Optimal Management of Renewable MG," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 1, 2013, pp. 11-18. doi: 10.4236/jilsa.2013.51002.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] T. Niknam and A. Kavousifard, “Impact of Thermal Recovery and Hydrogen Production of Fuel Cell Power Plants on Distribution Feeder Reconfiguration,” IET Generation, Transmission & Distribution, Vol. 6, No. 9, 2012, pp. 831-843. doi:10.1049/iet-gtd.2011.0775
[2] O. Hafez and K. Bhattacharya, “Optimal Planning and Design of a Renewable Energy Based Supply System 435 for Microgrids,” Renewable Energy, Vol. 45, 2012, pp. 7-15. doi:10.1016/j.renene.2012.01.087
[3] M. Pipattanasomporn, H. Feroze and S. Rahman, “Securing Critical Loads in a PV-Based Microgrid with a MultiAgent System,” Renewable Energy, Vol. 39, No. 1, 2012, pp. 166-174. doi:10.1016/j.renene.2011.07.049
[4] H. Morais, P. Kádár, P. Faria, Z. A. Vale and H. M. Khodr, “Optimal Scheduling of a Renewable Micro-Grid in an Isolated Load Area Using Mixed-Integer Linear Programming,” Renewable Energy, Vol. 35, No. 1, 2010, pp. 151-156. doi:10.1016/j.renene.2009.02.031
[5] A. G. Tsikalakis and N. D. Hatziargyriou, “Centralized Control for Optimizing Microgrids Operation,” IEEE Transactions on Energy Conversion, Vol. 23, No. 1, 2008, pp. 241-248. doi:10.1109/TEC.2007.914686
[6] H. M. Khodr, N. El Halabi and M. García-Gracia, “Intelligent Renewable Microgrid Scheduling Controlled by a Virtual Power Producer: A Laboratory Experience,” Renewable Energy, Vol. 48, 2012, pp. 269-275. doi:10.1016/j.renene.2012.05.008
[7] O. Hafez and K. Bhattacharya, “Optimal Planning and Design of a Renewable Energy Based Supply System for Microgrids,” Renewable Energy, Vol. 45, 2012, pp. 7-15. doi:10.1016/j.renene.2012.01.087
[8] R. Chedid and S. Raiman, “Unit Sizing and Control of Hybrid Wind Solar Power Systems,” IEEE Transactions on Energy Conversion, Vol. 12, No. 1, 1997, pp. 79-85. doi:10.1109/60.577284
[9] S. Chakraborty, M. D. Weiss and M. G. Simoes, “Distributed Intelligent Energy Management System for a SinglePhase High Frequency AC Microgrid,” IEEE Transactions on Industrial Electronics, Vol. 54, No. 1, 2007, pp. 97-109. doi:10.1109/TIE.2006.888766
[10] A. Dukpa, I. Dugga, B. Venkatesh and L. Chang, “Optimal Participation and Risk Mitigation of Wind Generators in an Electricity Market,” IET Renewable Power Generation, Vol. 4, No. 2, 2010, pp. 165-175. doi:10.1049/iet-rpg.2009.0016
[11] C. Chen, S. Duan, T. Cai, B. Liu and G. Hu, “Smart Energy Management System for Optimal Microgrid Economic Operation,” IET Renewable Power Generation, Vol. 5, No. 3, 2011, pp. 258-267. doi:10.1049/iet-rpg.2010.0052
[12] A. A. Moghaddam, A. Seifi, T. Niknam and M. R. A. Pahlavani, “Multi-Objective Operation Management of a Renewable MG (Micro-Grid) with Back-Up Micro-Turbine/Fuel Cell/Battery Hybrid Power Source,” Energy, Vol. 36, No. 11, 2011, pp. 6490-6507. doi:10.1016/j.energy.2011.09.017
[13] G. Komarasamy and A. Wahi, “An Optimized K-Means Clustering Technique Using Bat Algorithm,” European Journal of Scientific Research, Vol. 84, No. 2, 2012, pp. 263-273.
[14] T. Niknam, A. K. Fard and A. Seifi, “Distribution Feeder Reconfiguration Considering Fuel Cell/Wind/Photovoltaic Power Plants,” Renewable Energy, Vol. 37, No. 1, 2011, pp. 213-225. doi:10.1016/j.renene.2011.06.017
[15] T. Niknam, A. Kavousifard, S. Tabatabaei and J. Aghae, “Optimal Operation Management of Fuel Cell/Wind/Photovoltaic Power Sources Connected to Distribution Networks,” Journal of Power Sources, Vol. 196, No. 20, 2011, pp. 8881-8896. doi:10.1016/j.jpowsour.2011.05.081
[16] T. Niknam, A. Kavousifard and J. Aghaei, “ScenarioBased Multiobjective Distribution Feeder Reconfiguration Considering Wind Power Using Adaptive Modified Particle Swarm Optimization,” IET Renewable Power Generation, Vol. 6, No. 4, 2012, pp. 236-247. doi:10.1049/iet-rpg.2011.0256
[17] A. Kavousi-Fard and M. R. Akbari-Zadeh, “Reliability Enhancement Using Optimal Feeder Reconfiguration,” Neurocomputing, 2012, in Press. doi:10.1016/j.neucom.2012.08.033
[18] A. Kavousifard and H. Samet, “Consideration Effect of Uncertainty in Power System Reliability Indices Using Radial Basis Function Network and Fuzzy Logic Theory,” Neurocomputing, Vol. 74, No. 17, 2011, pp. 34203427. doi:10.1016/j.neucom.2011.05.017
[19] A. R. Malekpour, T. Niknam, A. Pahwa and A. K. Fard, “Multi-Objective Stochastic Distribution Feeder Reconfiguration in Systems With Wind Power Generators and Fuel Cells Using the Point Estimate Method,” IEEE Transactions on Power Systems, Vol. 99, 2012, pp. 1-10. doi:10.1109/TPWRS.2012.2218261
[20] S. H. Ling, “Hybrid Particle Swarm Optimization with Wavelet Mutation and Its Industrial Applications,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 38, No. 3, 2008, pp. 743-763.
[21] S. H. Ling and F. H. F. Leung., “An Improved Genetic Algorithm with Average-Bound Crossover and Wavelet Mutation Operations,” Soft Computing, Vol. 11, No. 1, 2007, pp. 7-31. doi:10.1007/s00500-006-0049-7

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.